Research On ECG Signal Reconstruction And Classification Algorithm Based On Millimeter Wave Radar

With the rapid development of electronic technology,a variety of noncontact sensors,such as radar and other new devices in the civilian sector is developing day by day.At the same time,with the advent of the aging era,the demand for comprehensive medical monitoring of the elderly is also increasing and has a broad market prospect.Radar as a non-contact sensor has higher convenience and adaptability compared to wearable devices,and can achieve medical monitoring of the elderly in more relaxed scenarios,such as target identification,respiratory heartbeat detection,ECG information monitoring and diagnosis.In this paper,medical monitoring algorithms for radar human vital signs are investigated,and the following aspects are carried out:1.a millimeter wave radar-based vital signs signal detection system is designed.The system consists of the following three steps,firstly,after signal processing the original radar echo signal is filtered for clutter and then the distance-velocity-angle 3D-FFT is used to obtain the target distance-velocity-angle information and output;secondly,by Kalman filtering the distance-velocity-angle 3D information,the correction and tracking of the target trajectory are realized and monitored in real time on the host computer interface;finally,when the system detects that the target enters the Finally,when the system detects that the target enters the respiratory heartbeat detection area,it extracts the phase of human respiratory heartbeat signal through signal processing,thus realizing the respiratory heartbeat separation and detection of human targets.2.A reconstruction algorithm of ECG signal based on CNN-BiLSTM deep learning network structure is proposed.The algorithm achieves the effect of reconstructing the radar echo signal into an ECG signal with medical diagnostic significance through the constructed CNN-BiLSTM network model by model training with the established radar signal filter and the ECG denoised radar-ECG signal dataset.The experimental results show that the important wave time error values of the reconstructed ECG signals are within the effective medical diagnosis range,and the correlation of the reconstructed signals reaches 0.873,which verifies that the algorithm has a good effect on the reconstruction of ECG signals.3.For the need of automatic classification and diagnosis of medical features of the reconstructed ECG signal,this paper proposes a convolutional neural network-based classification algorithm for the reconstructed ECG signal,in which the ECG signal generated by the original signal collected by radar after the ECG reconstruction algorithm and the arrhythmia database together form the ECG signal data set into the convolutional neural network for training and testing,so as to realize the The results demonstrate that the convolutional neural network used in this paper is a good solution for the classification and diagnosis of different pathological features.The experimental results demonstrate that the convolutional neural network ECG classification algorithm used in this paper achieves a good classification of various ECG signals with an accuracy of 99.31%.